Long noncoding RNAs are recently emerging as critical factors of tumorigenesis. Originally regarded as a pre-messenger RNA (mRNA) splicing regulator, the long noncoding RNA MALAT1 has been demonstrated to regulate gene transcription by binding histone modification enzymes and transcription factors, and to regulate mRNA and protein expression post-transcriptionally by binding microRNAs (miRNAs) and acting as a sponge. Early studies consistently report that MALAT1 is up-regulated in human cancer tissues of various organ origins, particularly metastatic cancer tissues, that high levels of MALAT1 expression in cancer tissues are associated with poor patient prognosis, and that MALAT1 induces cancer cell proliferation, migration, and invasion in vitro and tumor metastasis in mice. By contrast, by analyzing multiple independent large datasets, MALAT1 have very recently been found to be down-regulated in human colorectal and breast cancer tissues, and low MALAT1 expression is associated with decreased patient survival. By binding to the transcription factor TEAD, MALAT1 suppresses metastasis gene expression, colorectal and breast cancer cell migration, invasion, and metastasis in vitro and in mice. MALAT1 has therefore been proposed to function as a tumor suppressor in colorectal and breast cancers. More comprehensive studies with multiple independent cohorts of human cancer tissues of various organ origins, in vitro and in vivo function, and mechanism studies with rescue experiments are required to confirm the oncogenic or tumor suppressive role of MALAT1 in other cancers. Copyright © 2020 Chen, Zhu and Jin.Abnormal DNA methylation, an epigenetic modification, has increasingly been linked to the pathogenesis of many human cancers. However, there has been little focus on the DNA methylation patterns of genes encoding long noncoding RNAs (lncRNAs) in gastric cancer (GC). This study comprehensively determined DNA methylation and lncRNA expression profiles in GC through genome-wide analysis. Differentially methylated loci and lncRNAs were identified by integrating multi-omics data. In total, 548 differentially methylated CpG sites in lncRNA promoters and 2,399 differentially expressed lncRNAs were screened that were capable of distinguishing GC from normal tissues. Among them, 22 differentially methylation sites in 17 lncRNAs were inversely related to expression levels. Further analysis of DNA methylation status and gene expression level in GC revealed that three CpG sites (cg01550148, cg22497867, and cg20001829) and two lncRNAs (RP11-366F6.2 and RP5-881L22.5) were significantly associated with GC patient overall survival. Molecular function analysis showed that these abnormally methylated lncRNAs were mainly involved in transcriptional activator activity. Our study identified several lncRNAs regulated by aberrant DNA methylation that have clinical utility as novel prognostic biomarkers in GC. These findings help improve the understanding of methylated patterns of lncRNAs and further our knowledge of the role of epigenetics in cancer development. Copyright © 2020 Song, Wu and Guan.Background Anti-inflammatory cytokine polymorphisms in the transforming growth factor-β1 (TGF-β1), interleukin-4 (IL-4), and IL-10 genes have been implicated as risk factors for chronic kidney disease (CKD), but the results from published studies are inconsistent. Our meta-analysis reviews and summarizes the cumulative evidence for these associations. Methods A systematic literature search of five databases was performed up to October 2019. Two authors independently extracted data and evaluated the quality of included studies. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) were generated from random-effects or fixed-effects models using Stata 12.0. https://www.selleckchem.com/products/sbe-b-cd.html Results Nineteen studies from 10 countries satisfied our inclusion criteria and were included in the meta-analysis. Overall, the pooled analysis showed that TGF-β1 rs1800469 was associated with decreased susceptibility to CKD (CC + TC vs. TT, OR = 0.33, 95% CI 0.15-0.76, P = 0.009; CC vs. TT, OR = 0.33, 95% CI 0.15-0.73, P = 0.006), whereas TGF-β1 rs1800471 was associated with increased CKD susceptibility (CC vs. CG + GG, OR = 1.68, 95% CI 1.02-2.77, P = 0.041). In stratified analyses based on ethnicity, TGF-β1 rs1800469 was associated with CKD susceptibility in Asians and Caucasians, and there was an association of TGF-β1 rs1800470 and IL-4 rs8179190 with CKD in Asians. Stratified analyses also associated TGF-β1 rs1800471 with CKD susceptibility in Caucasians. Neither overall meta-analyses nor stratified analyses identified an association of the IL-10 rs1800869 and rs1800871 polymorphisms with susceptibility to CKD. Conclusions Available data suggest that common polymorphisms in the TGF-β1 and IL-4 genes including rs1800469, rs1800470, rs1800471, and rs8179190 may be important genetic contributors to CKD susceptibility. Copyright © 2020 Mai, Jiang, Wu, Liu, Zhu and Zhu.The ability to predict the drug response for cancer disease based on genomics information is an essential problem in modern oncology, leading to personalized treatment. By predicting accurate anticancer responses, oncologists achieve a complete understanding of the effective treatment for each patient. In this paper, we present DSPLMF (Drug Sensitivity Prediction using Logistic Matrix Factorization) approach based on Recommender Systems. DSPLMF focuses on discovering effective features of cell lines and drugs for computing the probability of the cell lines are sensitive to drugs by logistic matrix factorization approach. Since similar cell lines and similar drugs may have similar drug responses and incorporating similarities between cell lines and drugs can potentially improve the drug response prediction, gene expression profile, copy number alteration, and single-nucleotide mutation information are used for cell line similarity and chemical structures of drugs are used for drug similarity. Evaluation of the proposed method on CCLE and GDSC datasets and comparison with some of the state-of-the-art methods indicates that the result of DSPLMF is significantly more accurate and more efficient than these methods. To demonstrate the ability of the proposed method, the obtained latent vectors are used to identify subtypes of cancer of the cell line and the predicted IC50 values are used to depict drug-pathway associations. The source code of DSPLMF method is available in https//github.com/emdadi/DSPLMF. Copyright © 2020 Emdadi and Eslahchi.